Operators in mule dataweave
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This document discusses common operations in Mule Dataweave including map, pluck, filter, and remove. Map applies a function to each element of an array and returns a new array. Pluck maps an object to an array by applying a function to each key-value pair. Filter returns an array or object containing only elements that pass the criteria in the provided function. Remove returns an array with specified indexes removed.
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class Currency {
protected:
int whole;
int fraction;
virtual std::string get_name() = 0;
public:
Currency() {
whole = 0;
fraction = 0;
}
Currency(double value) {
if (value < 0)
throw "Invalid value";
whole = int(value);
fraction = std::round(100 * (value - whole));
}
Currency(const Currency& curr) {
whole = curr.whole;
fraction = curr.fraction;
}
/* This algorithm gets the whole part or fractional part of the currency
Pre: whole, fraction - integer numbers
Post:
Return: whole or fraction
*/
int get_whole() { return whole; }
int get_fraction() { return fraction; }
/* This algorithm adds an object to the same currency
Pre: object (same currency)
Post:
Return:
*/
void set_whole(int w) {
if (w >= 0)
whole = w;
}
void set_fraction(int f) {
if (f >= 0 && f < 100)
fraction = f;
}
/* This algorithm adds an object to the same currency
Pre: object (same currency)
Post:
Return:
*/
void add(const Currency* curr) {
whole += curr->whole;
fraction += curr->fraction;
if (fraction > 100) {
whole++;
fraction %= 100;
}
}
/* This algorithm subtracts an object to the same currency
Pre: object (same currency)
Post:
Return:
*/
void subtract(const Currency* curr) {
if (!isGreater(*curr))
throw "Invalid Subtraction";
whole -= curr->whole;
if (fraction < curr->fraction) {
fraction = fraction + 100 - curr->fraction;
whole--;
}
else {
fraction -= curr->fraction;
}
}
/* This algorithm compares the an object of the same currency for equality or inequality
Pre: object (same currency)
Post:
Return: whole/fraction
*/
bool isEqual(const Currency& curr) {
return curr.whole == whole && curr.fraction == fraction;
}
/* This algorithm compares the an object of the same currency to determine which is greater or
smaller
Pre: object (same currency)
Post:
Return: true/false
*/
bool isGreater(const Currency& curr) {
if (whole < curr.whole)
return false;
if (whole == curr.whole && fraction < curr.fraction)
return false;
return true;
}
/* This algorithm prints the name and value of the currency object
Pre: value of whole, fraction, and the name
Post: whole, fraction, get_name()
Return:
*/
void print() {
std::cout << whole << "." << fraction << " " << get_name() << std::endl;
}
};
class Krone : public Currency {
protected:
/*
This algorithm gets the name for the Currency.
Pre: name - declared as string and initialized as Krone
Post:
Return: name
*/
std::string name = "Krone";
std::string get_name() {
return name;
}
public:
Krone() : Currency() { }
Krone(double value) : Currency(value) { }
Krone(Krone& curr) : Currency(curr) { }
};
class Soum : public Currency {
protected:
/* This algorithm gets the name for the Currency.
Pre: name - declared as string and initialized as Soum
Post:
Return: name
*/
std::string name = "Soum";
std::string get_name() {
return name;
}
public:
Soum() : Currency() { }
Soum(double value) : Currency(value) { }
Soum(Krone& curr) : Currency(curr) { }
};
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Exploring Streams and Lambdas in Java8
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Anypoint connectorsRamakrishna kapa Anypoint Connectors allow Mule to communicate with external systems by receiving or sending messages. There are two types of connectors: endpoint-based connectors follow request-response patterns and are named after protocols like FTP; operation-based connectors perform specific third-party API operations. Connectors can act as message sources, processors, or destinations. Some require global configuration for connection details shared across multiple uses of the same connector type.
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Batch processingRamakrishna kapa Mule allows processing of messages in batches through batch jobs. A batch job splits incoming messages into individual records, processes each record using message processors, and reports results. Batch processing is useful for streaming data integration, synchronizing data between applications like Salesforce and Netsuite, ETL processes, and handling large API data. A batch job exists outside flows and contains steps to sequentially process records through variables and MEL expressions.
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Msmq connectivityRamakrishna kapa The document provides steps to configure connectivity between Mule and MSMQ (Microsoft Message Queuing) by installing and configuring the MSMQ connector in Mule. It discusses that the MSMQ connector allows sending and receiving data from MSMQ queues via the Anypoint Gateway for Windows. It then outlines the steps to download and install the Windows Gateway Services, .NET 4.0, MSMQ services, and the MSMQ connector in Mule. Finally, it mentions running a sample application which involves setting up a demo queue in MSMQ and configuring a Mule project.
Scopes in mule
Scopes in muleRamakrishna kapa Scopes in mule allow you to group message processors together. Certain scopes like Poll, Message Enricher, and Until Successful allow only one embedded message processor. Scopes can execute synchronously, halting the parent flow until the child scope completes, or asynchronously where the parent flow continues independently. The Cache Scope stores and reuses frequently called data, and the Composite Source handles messages from multiple sources. The Foreach splits messages into parts for processing while the Poll periodically retrieves messages. The Transactional scope executes a series of steps atomically, while the Until Successful retries an embedded processor until it succeeds or hits retry limits.
Data weave more operations
Data weave more operationsRamakrishna kapa This document describes several string matching operations in DataWeave including matches, startsWith, endsWith, find, match, and similar. It provides examples of the operations by showing DataWeave code that defines the operation, applies it to sample data, and outputs the results as JSON.
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Basic math operations using dataweaveRamakrishna kapa This document demonstrates how to perform basic math operations like addition, subtraction, multiplication, division, finding maximum and minimum values, rounding, and square root functions using DataWeave. It provides code examples for each operation, showing how to transform and output the results in JSON or XML format.
Dataweave types operators
Dataweave types operatorsRamakrishna kapa This document summarizes various data weave operators for coercing data types and transforming data. It describes operators for coercing data to strings, numbers, and dates. It also covers the flatten and split by operators for transforming array and string data.
Data weave in mule
Data weave in muleRamakrishna kapa DataWeave is Mule's tool for transforming data between different formats. The Transform Message component uses a DataWeave script to transform the incoming Mule message. DataWeave scripts contain a header section defining directives and an output structure, and a body section describing the transformations using the DataWeave syntax. DataWeave allows mapping between input and output fields visually or by writing code to aggregate, normalize, group, join, partition, pivot and filter data.
Servicenow connector
Servicenow connectorRamakrishna kapa The Mule ESB Servicenow connector allows integration between the ServiceNow IT service management platform and other applications by automating IT services and enabling data exchange. It provides over 30 operations to interact with the ServiceNow API to manage incidents, requests, and users. Developers can use the connector to create and fetch ServiceNow data from anywhere.
Introduction to testing mule
Introduction to testing muleRamakrishna kapa Unit testing validates individual units of code, like Mule flows, to check their correctness in isolation. Integration testing validates how units of code work together by testing entire applications or groups of interconnected flows. The document provides guidance on unit and integration testing for Mule applications, including writing modular, short flows; using environments for integration tests; and parameterizing code with placeholders.
Choice flow control
Choice flow controlRamakrishna kapa The choice flow control routes messages based on evaluating expressions against message properties or payload. It provides conditional logic similar to if/then/else statements, directing messages to the first routing option whose configuration evaluates to true, or to a default option if none match. Configuring it requires determining the evaluation criteria, routing options, processing for each, and default routing.
Message enricher example
Message enricher exampleRamakrishna kapa The Mule Message Enricher allows the current message to be enriched by invoking an enrichment resource, which can be any message processor. The enrichment resource runs independently and modifies a copy of the message rather than the original message. The enricher supports enriching flow variables and message headers. It will leave the original message untouched if the enrichment resource does not return a result.
Mule exception strategies
Mule exception strategiesRamakrishna kapa Mule applications have two types of exceptions: system exceptions and messaging exceptions. System exceptions occur at the application level while no message is involved. Messaging exceptions occur when processing a message through a Mule flow. There are several exception strategies in Mule including catch, rollback, reference, and choice strategies. Catch strategies consume messages while rollback strategies roll messages back for reprocessing. Choice strategies handle exceptions based on message content.
Anypoint connector basics
Anypoint connector basicsRamakrishna kapa The Anypoint Connector DevKit is a tool that makes building reusable connectors easier. It uses annotations and code generation to handle boilerplate code needed to integrate connectors with Mule runtime and Anypoint Studio. Connectors allow communication between external resources and Mule applications, transforming data between the two. DevKit abstracts this communication and generates user interfaces, simplifying connector usage. Well-built connectors make app development simpler by handling tasks like pagination and session handling.
Mule global elements
Mule global elementsRamakrishna kapa Global elements in Mule allow you to configure elements once and then reference them multiple times from flows, rather than repeatedly writing the same code. They provide flexibility by allowing elements to use a global element's configuration exactly or with modifications, and they are reusable by configuring an element once and sharing it. Global elements also offer efficiency by changing a configuration in one place and having it apply to all elements that reference it.
Mule message structure and varibles scopes
Mule message structure and varibles scopesRamakrishna kapa The document discusses Mule message structure and variable scopes. A Mule message contains a header and payload. It is embedded in a Mule message object that can include variables, attachments, and exceptions. Properties have inbound and outbound scopes, with inbound being immutable metadata from the source and outbound being mutable metadata set during a flow. Variables have flow, session, and record scopes and are temporary pieces of information about a message that are not passed through the entire flow. Session variables apply across all flows in an application.
How to create an api in mule
How to create an api in muleRamakrishna kapa To create an API in Mule, first create a Mule project called "Sample API". Then create a RAML definition file called "test.raml" to define the API. Write the RAML and generate flows from it in Studio. This will create the flows automatically. Run the project as a Mule application. The API can then be accessed at http://localhost:8081/api/console/ and the GET method will return a response.
Log4j is a reliable, fast and flexible
Log4j is a reliable, fast and flexibleRamakrishna kapa Log4j is a popular Java logging framework that provides reliable and fast logging. It follows a layered architecture with core objects like loggers, appenders, and layouts. Loggers capture log information, appenders publish it to destinations like files or databases, and layouts format the information. The log4j.properties configuration file defines the logging level, appenders, and their layout patterns. For example, it can set the root logger level to DEBUG and direct DEBUG logs to a file appender that writes to a log file using a %m%n pattern.
Jsf 2
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- 1. Operators in Mule Dataweave In DataWeave you can carry out many different operations on the elements of a DataWeave transform.
- 2. Map • Using Map to Return an Array • (':array', ':function') ⇒ :array • Returns an array that is the result of applying a transformation function (lambda) to each of the elements. The lambda is invoked with two parameters: index and the value. If these parameters are not named, the index is defined by default as $$ and the value as $
- 4. output
- 5. • Pluck • (':object', ':function') ⇒ ':array' • Pluck is useful for mapping an object into an array. Pluck is an alternate mapping mechanism to mapObject. Like mapObject, pluck executes a lambda over every key:value pair in its processed object, but instead of returning an object, it returns an array, which may be built from either the values or the keys in the object. • The lambda is invoked with two parameters: key and the value. If these parameters are not named, the key is defined by default as $$ and the value as $.
- 6. Filter • Using Filter on an Array • (':array', ':function') ⇒ ':array' • Returns an array that only contains those that pass the criteria specified in the lambda. The lambda is invoked with two parameters: index and the value. If these parameters are not named, the index is defined by default as $$ and the value as $.
- 8. output
- 9. Using Filter on an Object • (':object', ':function') ⇒ ':object' • Returns an object with the key:value pairs that pass the acceptance criteria defined in the lambda. If these parameters are not named, the index is defined by default as $$ and the value as $.
- 11. output
- 12. Remove • Using Remove on an Array • (':array', ':name') ⇒ ':array' • When running it on an array, it returns another array where the specified indexes are removed.